In spatially explicit models of mobile agents, in contrast to the idealized mean-field or network models, the potential contagion interactions are determined by a switching proximity network that changes in time as the individuals follow a specific set of spatial dynamical rules (for an example, see [1]}).
The core parameters of a typical mean-field SIR-contagion process, such as the contact rate and contact duration, then become emergent features of the self-organized spatial dynamics, as it was shown for gas-like systems of self-propelled active Brownian particles in [2]}, [3]}, [4]}, [5]}.
The properties of the spatial dynamics, such as the speed of the individual agents considered in [4]}, can thus affect in nontrivial ways the global contagion process.
In these examples, however, the agents or particles do not form spatial structures, displaying instead homogeneous density and isotropic orientations, in contrast to what is known to occur in many models of self-propelled agents and in real-world systems.
In particular, in models where spatial interactions such as velocity alignment forces between neighboring agents are introduced, we expect the formation of states that display long-range order (leading to coordinated collective movement at the scale of the system) and a variety of spatial structures (such as large-scale, high density bands or clusters) [7]}, [8]}, [9]}, [10]}.
To properly describe the contagion process, we must therefore consider the various degrees of mixing and density distributions exhibited by different self-organized states.
